Estimating Personalized Drug Responses from Real World Evidence

ABSTRACT

A mechanism is provided in a data processing system comprising at least one processor and at least one memory, the at least one memory comprising instructions executed by the at least one processor to cause the at least one processor to implement a drug response estimation engine. The drug response estimation engine receives real-world evidence for a plurality of patients. A patient similarity network builder component executing within the drug response estimation engine builds a patient similarity network. A regression analysis component executing within the drug response estimation engine builds a network localized regression analysis approach. A patient clustering component executing within the drug response estimation engine groups patients based on demographics and comorbidities to form a plurality of patient clusters. The drug response estimation engine estimates drug responses for a given patient based on the patient similarity network, the network localized regression analysis approach, and the plurality of patient clusters The drug response estimation engine outputs the drug responses for the given patient.

BACKGROUND

The present application relates generally to an improved data processingapparatus and method and more specifically to mechanisms for estimatingpersonalized drug responses from real world evidence.

Decision-support systems exist in many different industries where humanexperts require assistance in retrieving and analyzing information. Anexample that will be used throughout this application is a diagnosissystem employed in the healthcare industry. Diagnosis systems can beclassified into systems that use structured knowledge, systems that useunstructured knowledge, and systems that use clinical decision formulas,rules, trees, or algorithms. The earliest diagnosis systems usedstructured knowledge or classical, manually constructed knowledge bases.The Internist-I system developed in the 1970s uses disease-findingrelations and disease-disease relations. The MYCIN system for diagnosinginfectious diseases, also developed in the 1970s, uses structuredknowledge in the form of production rules, stating that if certain factsare true, then one can conclude certain other facts with a givencertainty factor. DXplain, developed starting in the 1980s, usesstructured knowledge similar to that of Internist-I, but adds ahierarchical lexicon of findings.

Iliad, developed starting in the 1990s, adds more sophisticatedprobabilistic reasoning where each disease has an associated a prioriprobability of the disease (in the population for which Iliad wasdesigned), and a list of findings along with the fraction of patientswith the disease who have the finding (sensitivity), and the fraction ofpatients without the disease who have the finding (1-specificity).

In 2000, diagnosis systems using unstructured knowledge started toappear. These systems use some structuring of knowledge such as, forexample, entities such as findings and disorders being tagged indocuments to facilitate retrieval. ISABEL, for example, uses Autonomyinformation retrieval software and a database of medical textbooks toretrieve appropriate diagnoses given input findings. Autonomy Auminenceuses the Autonomy technology to retrieve diagnoses given findings andorganizes the diagnoses by body system. First CONSULT allows one tosearch a large collection of medical books, journals, and guidelines bychief complaints and age group to arrive at possible diagnoses. PEPIDDDX is a diagnosis generator based on PEPID's independent clinicalcontent.

Clinical decision rules have been developed for a number of medicaldisorders, and computer systems have been developed to helppractitioners and patients apply these rules. The Acute Cardiac IschemiaTime-Insensitive Predictive Instrument (ACI-TIPI) takes clinical and ECGfeatures as input and produces probability of acute cardiac ischemia asoutput to assist with triage of patients with chest pain or othersymptoms suggestive of acute cardiac ischemia. ACI-TIPI is incorporatedinto many commercial heart monitors/defibrillators. The CaseWalkersystem uses a four-item questionnaire to diagnose major depressivedisorder. The PKC Advisor provides guidance on 98 patient problems suchas abdominal pain and vomiting.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described herein in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

In one illustrative embodiment, a method is provided in a dataprocessing system comprising at least one processor and at least onememory, the at least one memory comprising instructions executed by theat least one processor to cause the at least one processor to implementa drug response estimation engine. The drug response estimation engineoperates to receive real-world evidence for a plurality of patients. Apatient similarity network builder component executing within the drugresponse estimation engine builds a patient similarity network. Aregression analysis component executing within the drug responseestimation engine builds a network localized regression analysisapproach. A patient clustering component executing within the drugresponse estimation engine groups patients based on demographics andcomorbidities to form a plurality of patient clusters. The drug responseestimation engine estimates drug responses for a given patient based onthe patient similarity network, the network localized regressionanalysis approach, and the plurality of patient clusters. The drugresponse estimation engine outputs the drug responses for the givenpatient.

In other illustrative embodiments, a computer program product comprisinga computer useable or readable medium having a computer readable programis provided. The computer readable program, when executed on a computingdevice, causes the computing device to perform various ones of, andcombinations of, the operations outlined above with regard to the methodillustrative embodiment.

In yet another illustrative embodiment, a system/apparatus is provided.The system/apparatus may comprise one or more processors and a memorycoupled to the one or more processors. The memory may compriseinstructions which, when executed by the one or more processors, causethe one or more processors to perform various ones of, and combinationsof, the operations outlined above with regard to the method illustrativeembodiment.

These and other features and advantages of the present invention will bedescribed in, or will become apparent to those of ordinary skill in theart in view of, the following detailed description of the exampleembodiments of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention, as well as a preferred mode of use and further objectivesand advantages thereof, will best be understood by reference to thefollowing detailed description of illustrative embodiments when read inconjunction with the accompanying drawings, wherein:

FIG. 1 depicts a schematic diagram of one illustrative embodiment of acognitive healthcare system in a computer network;

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented;

FIG. 3 is an example diagram illustrating an interaction of elements ofa healthcare cognitive system in accordance with one illustrativeembodiment;

FIGS. 4A and 4B are graphs illustrating a longitudinal patient historyof drug exposures in accordance with an illustrative embodiment;

FIG. 5 depicts an algorithm for iteratively reweighted least squares forlocalized lasso regularization in accordance with an illustrativeembodiment;

FIG. 6 is a block diagram illustrating a drug response estimation enginein accordance with an illustrative embodiment; and

FIG. 7 is a flowchart illustrating operation of a drug responseestimation engine in accordance with an illustrative embodiment.

DETAILED DESCRIPTION

The strengths of current cognitive systems, such as current medicaldiagnosis, patient health management, patient treatment recommendationsystems, law enforcement investigation systems, and other decisionsupport systems, are that they can provide insights that improve thedecision making performed by human beings. For example, in the medicalcontext, such cognitive systems may improve medical practitioners'diagnostic hypotheses, can help medical practitioners avoid missingimportant diagnoses, and can assist medical practitioners withdetermining appropriate treatments for specific diseases. However,current systems still suffer from significant drawbacks which should beaddressed in order to make such systems more accurate and usable for avariety of applications as well as more representative of the way inwhich human beings make decisions, such as diagnosing and treatingpatients. In particular, one drawback of current systems is that not allpatients in a patient group or cohort have similar responses to drugs.Thus, while patient cohort analysis may provide a general recommendationas to drug treatment options for patients in order to provide propertreatment of individual patients within the cohort, it is important totake into consideration their own personal characteristics and potentialfor adverse drug reactions (ADRs) to the drug options. This is aprecision medicine problem.

Predicting the responses of drugs, both positive (e.g., therapeuticeffects) and negative (e.g., adverse drug reactions (ADRs)) is animportant problem for finding new effective and safe drugs. Real-worldevidence (e.g., electronic medical records (EMRs), claims) can be usedfor finding drug responses that were not considered and/or tested duringthe drug design phase. Furthermore, a drug might have heterogeneouseffects on different patients. Estimating the personalized effects ofdifferent drugs on different patients is important for personalized andprecise medicine. Previous approaches to finding drug responses fromreal-world evidence are mostly on a global scale, i.e., they considerall patients together and, thus, are not able to predict the responsesof drugs for an individual patient.

The illustrative embodiments provide mechanisms for estimatingpersonalized drug responses from real world evidence. The illustrativeembodiments leverage baseline regularization framework for finding theresponses of drugs for a particular outcome (e.g., lab tests change,adverse drug reactions happen). Moreover, the illustrative embodimentsprovide personalized drug response estimation from real-world evidencebased on localized LASSO regression analysis built on patientsimilarities.

In contrast to the one-size-fits-all medicine, personalized medicineaims to tailor treatment to the individual characteristics of eachpatient. This requires the ability to classify patients into subgroupswith predictable response to a specific treatment. Although there arealready many examples of personalized medicine by leveraginggenetics/genomics information in current practice, such information isnot yet widely available in everyday clinical practice, and isinsufficient since it only addresses one of many factors affectingresponse to medication. Large-scale longitudinal observation data suchas Electronic Health Records (EHRs) contains millions of patient recordsand thus, provides a unique opportunity to reassess the effects of adrug from many different perspectives. For example, a new area ofresearch has emerged to find both the positive effect of drugs that arealready in use in terms of their ability in reducing the laboratory testmeasurement, and the negative effect of those drugs by assessing thepotential risks of causing adverse drug reactions (ADRs).

Most of the existing studies aim to apply a linear model to estimatedrug effects for a certain type of outcome of interest such as decreasedcancer risk, decreased fasting blood glucose, increased risk of ADRs.These models consider all drugs simultaneously into a linear fixedeffect model to account for the effect of confounders. They alsoleverage the longitudinal patient data using patient's own previous drugresponses as control, hence these methods are called Self-ControlledCase Series (SCCS) model. Recently, a baseline regularization model hasbeen proposed to utilize the drug histories over time using a baselineparameter in the model which can account for the variations oflaboratory test results (the outcome of interest) among differentpatients. However, none of these studies can handle the patientheterogeneity and estimate personalized drug effects. In EHRs, thereexist huge amount of variations among the patients' characteristics andtheir ability to respond to a drug. For example, certain group ofpatients with chronic health conditions can respond to a drug in acertain manner than another group of patient with a different set ofchronic health conditions. Such patient heterogeneity needs to be takeninto account while identifying drug effects, so that the obtained drugswith possible therapeutic indications and/or ADRs can be applied in morepersonalized manner during clinical decision making.

The illustrative embodiments propose a personalized drug responseprediction model to identify unique response patterns of each individualpatient using the longitudinal patient record. In particular, the modeluses separate parameters for each individual patient which represent thedrug effects on an outcome of interest. The model accounts for patientheterogeneity while building predictive models for identifying drugeffects. The illustrative embodiments provide the following:

A linear model that can account for the patients' heterogeneity in termsof how they respond to a particular set of drugs, which generalizes theoriginal baseline regularization model.

Several regularization schemes as additional loss functions, so thatover parameterization of personalized drug response model can beavoided. Using one such network regularization approach, the model canfurther cluster the patients automatically into multiple coherentgroups.

An iterative gradient descend based approach for solving the convexoptimization problem.

Before beginning the discussion of the various aspects of theillustrative embodiments in more detail, it should first be appreciatedthat throughout this description the term “mechanism” will be used torefer to elements of the present invention that perform variousoperations, functions, and the like. A “mechanism,” as the term is usedherein, may be an implementation of the functions or aspects of theillustrative embodiments in the form of an apparatus, a procedure, or acomputer program product. In the case of a procedure, the procedure isimplemented by one or more devices, apparatus, computers, dataprocessing systems, or the like. In the case of a computer programproduct, the logic represented by computer code or instructions embodiedin or on the computer program product is executed by one or morehardware devices in order to implement the functionality or perform theoperations associated with the specific “mechanism.” Thus, themechanisms described herein may be implemented as specialized hardware,software executing on general purpose hardware, software instructionsstored on a medium such that the instructions are readily executable byspecialized or general purpose hardware, a procedure or method forexecuting the functions, or a combination of any of the above.

The present description and claims may make use of the terms “a”, “atleast one of”, and “one or more of” with regard to particular featuresand elements of the illustrative embodiments. It should be appreciatedthat these terms and phrases are intended to state that there is atleast one of the particular feature or element present in the particularillustrative embodiment, but that more than one can also be present.That is, these terms/phrases are not intended to limit the descriptionor claims to a single feature/element being present or require that aplurality of such features/elements be present. To the contrary, theseterms/phrases only require at least a single feature/element with thepossibility of a plurality of such features/elements being within thescope of the description and claims.

Moreover, it should be appreciated that the use of the term “engine,” ifused herein with regard to describing embodiments and features of theinvention, is not intended to be limiting of any particularimplementation for accomplishing and/or performing the actions, steps,processes, etc., attributable to and/or performed by the engine. Anengine may be, but is not limited to, software, hardware and/or firmwareor any combination thereof that performs the specified functionsincluding, but not limited to, any use of a general and/or specializedprocessor in combination with appropriate software loaded or stored in amachine readable memory and executed by the processor. Further, any nameassociated with a particular engine is, unless otherwise specified, forpurposes of convenience of reference and not intended to be limiting toa specific implementation. Additionally, any functionality attributed toan engine may be equally performed by multiple engines, incorporatedinto and/or combined with the functionality of another engine of thesame or different type, or distributed across one or more engines ofvarious configurations.

In addition, it should be appreciated that the following descriptionuses a plurality of various examples for various elements of theillustrative embodiments to further illustrate example implementationsof the illustrative embodiments and to aid in the understanding of themechanisms of the illustrative embodiments. These examples intended tobe non-limiting and are not exhaustive of the various possibilities forimplementing the mechanisms of the illustrative embodiments. It will beapparent to those of ordinary skill in the art in view of the presentdescription that there are many other alternative implementations forthese various elements that may be utilized in addition to, or inreplacement of, the examples provided herein without departing from thespirit and scope of the present invention.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Java, Smalltalk, C++ or the like,and conventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

As noted above, the present invention provides mechanisms for estimatingpersonalized drug responses from real-world evidence. The illustrativeembodiments may be utilized in many different types of data processingenvironments. In order to provide a context for the description of thespecific elements and functionality of the illustrative embodiments,FIGS. 1-3 are provided hereafter as example environments in whichaspects of the illustrative embodiments may be implemented. It should beappreciated that FIGS. 1-3 are only examples and are not intended toassert or imply any limitation with regard to the environments in whichaspects or embodiments of the present invention may be implemented. Manymodifications to the depicted environments may be made without departingfrom the spirit and scope of the present invention.

FIGS. 1-3 are directed to describing an example cognitive system forhealthcare applications (also referred to herein as a “healthcarecognitive system”) which implements a request processing pipeline, suchas a Question Answering (QA) pipeline (also referred to as aQuestion/Answer pipeline or Question and Answer pipeline) for example,request processing methodology, and request processing computer programproduct with which the mechanisms of the illustrative embodiments areimplemented. These requests may be provided as structure or unstructuredrequest messages, natural language questions, or any other suitableformat for requesting an operation to be performed by the healthcarecognitive system. As described in more detail hereafter, the particularhealthcare application that is implemented in the cognitive system ofthe present invention is a healthcare application for estimatingpersonalized drug response from real-world evidence.

It should be appreciated that the healthcare cognitive system, whileshown as having a single request processing pipeline in the exampleshereafter, may in fact have multiple request processing pipelines. Eachrequest processing pipeline may be separately trained and/or configuredto process requests associated with different domains or be configuredto perform the same or different analysis on input requests (orquestions in implementations using a QA pipeline), depending on thedesired implementation. For example, in some cases, a first requestprocessing pipeline may be trained to operate on input requests directedto a first medical malady domain (e.g., various types of blood diseases)while another request processing pipeline may be trained to answer inputrequests in another medical malady domain (e.g., various types ofcancers). In other cases, for example, the request processing pipelinesmay be configured to provide different types of cognitive functions orsupport different types of healthcare applications, such as one requestprocessing pipeline being used for patient diagnosis, another requestprocessing pipeline being configured for medical treatmentrecommendation, another request processing pipeline being configured forpatient monitoring, etc.

Moreover, each request processing pipeline may have their own associatedcorpus or corpora that they ingest and operate on, e.g., one corpus forblood disease domain documents and another corpus for cancer diagnosticsdomain related documents in the above examples. In some cases, therequest processing pipelines may each operate on the same domain ofinput questions but may have different configurations, e.g., differentannotators or differently trained annotators, such that differentanalysis and potential answers are generated. The healthcare cognitivesystem may provide additional logic for routing input questions to theappropriate request processing pipeline, such as based on a determineddomain of the input request, combining and evaluating final resultsgenerated by the processing performed by multiple request processingpipelines, and other control and interaction logic that facilitates theutilization of multiple request processing pipelines.

As noted above, one type of request processing pipeline with which themechanisms of the illustrative embodiments may be utilized is a QuestionAnswering (QA) pipeline. The description of example embodiments of thepresent invention hereafter will utilize a QA pipeline as an example ofa request processing pipeline that may be augmented to includemechanisms in accordance with one or more illustrative embodiments. Itshould be appreciated that while the present invention will be describedin the context of the cognitive system implementing one or more QApipelines that operate on an input question, the illustrativeembodiments are not limited to such. Rather, the mechanisms of theillustrative embodiments may operate on requests that are not posed as“questions” but are formatted as requests for the cognitive system toperform cognitive operations on a specified set of input data using theassociated corpus or corpora and the specific configuration informationused to configure the cognitive system. For example, rather than askinga natural language question of “What diagnosis applies to patient P?”the cognitive system may instead receive a request of “generatediagnosis for patient P,” or the like. It should be appreciated that themechanisms of the QA system pipeline may operate on requests in asimilar manner to that of input natural language questions with minormodifications. In fact, in some cases, a request may be converted to anatural language question for processing by the QA system pipelines ifdesired for the particular implementation.

As will be discussed in greater detail hereafter, the illustrativeembodiments may be integrated in, augment, and extend the functionalityof these QA pipeline, or request processing pipeline, mechanisms of ahealthcare cognitive system with regard to estimating personalized drugresponses from real-world evidence.

Thus, it is important to first have an understanding of how cognitivesystems and question and answer creation in a cognitive systemimplementing a QA pipeline is implemented before describing how themechanisms of the illustrative embodiments are integrated in and augmentsuch cognitive systems and request processing pipeline, or QA pipeline,mechanisms. It should be appreciated that the mechanisms described inFIGS. 1-3 are only examples and are not intended to state or imply anylimitation with regard to the type of cognitive system mechanisms withwhich the illustrative embodiments are implemented. Many modificationsto the example cognitive system shown in FIGS. 1-3 may be implemented invarious embodiments of the present invention without departing from thespirit and scope of the present invention.

As an overview, a cognitive system is a specialized computer system, orset of computer systems, configured with hardware and/or software logic(in combination with hardware logic upon which the software executes) toemulate human cognitive functions. These cognitive systems applyhuman-like characteristics to conveying and manipulating ideas which,when combined with the inherent strengths of digital computing, cansolve problems with high accuracy and resilience on a large scale. Acognitive system performs one or more computer-implemented cognitiveoperations that approximate a human thought process as well as enablepeople and machines to interact in a more natural manner so as to extendand magnify human expertise and cognition. A cognitive system comprisesartificial intelligence logic, such as natural language processing (NLP)based logic, for example, and machine learning logic, which may beprovided as specialized hardware, software executed on hardware, or anycombination of specialized hardware and software executed on hardware.The logic of the cognitive system implements the cognitive operation(s),examples of which include, but are not limited to, question answering,identification of related concepts within different portions of contentin a corpus, intelligent search algorithms, such as Internet web pagesearches, for example, medical diagnostic and treatment recommendations,and other types of recommendation generation, e.g., items of interest toa particular user, potential new contact recommendations, or the like.

IBM Watson™ is an example of one such cognitive system which can processhuman readable language and identify inferences between text passageswith human-like high accuracy at speeds far faster than human beings andon a larger scale. In general, such cognitive systems are able toperform the following functions:

Navigate the complexities of human language and understanding

Ingest and process vast amounts of structured and unstructured data

Generate and evaluate hypothesis

Weigh and evaluate responses that are based only on relevant evidence

Provide situation-specific advice, insights, and guidance

Improve knowledge and learn with each iteration and interaction throughmachine learning processes

Enable decision making at the point of impact (contextual guidance)

Scale in proportion to the task

Extend and magnify human expertise and cognition

Identify resonating, human-like attributes and traits from naturallanguage

Deduce various language specific or agnostic attributes from naturallanguage

High degree of relevant recollection from data points (images, text,voice) (memorization and recall)

Predict and sense with situational awareness that mimic human cognitionbased on experiences

Answer questions based on natural language and specific evidence

In one aspect, cognitive systems provide mechanisms for answeringquestions posed to these cognitive systems using a Question Answeringpipeline or system (QA system) and/or process requests which may or maynot be posed as natural language questions. The QA pipeline or system isan artificial intelligence application executing on data processinghardware that answers questions pertaining to a given subject-matterdomain presented in natural language. The QA pipeline receives inputsfrom various sources including input over a network, a corpus ofelectronic documents or other data, data from a content creator,information from one or more content users, and other such inputs fromother possible sources of input. Data storage devices store the corpusof data. A content creator creates content in a document for use as partof a corpus of data with the QA pipeline. The document may include anyfile, text, article, or source of data for use in the QA system. Forexample, a QA pipeline accesses a body of knowledge about the domain, orsubject matter area, e.g., financial domain, medical domain, legaldomain, etc., where the body of knowledge (knowledgebase) can beorganized in a variety of configurations, e.g., a structured repositoryof domain-specific information, such as ontologies, or unstructured datarelated to the domain, or a collection of natural language documentsabout the domain.

Content users input questions to cognitive system which implements theQA pipeline. The QA pipeline then answers the input questions using thecontent in the corpus of data by evaluating documents, sections ofdocuments, portions of data in the corpus, or the like. When a processevaluates a given section of a document for semantic content, theprocess can use a variety of conventions to query such document from theQA pipeline, e.g., sending the query to the QA pipeline as a well-formedquestion which is then interpreted by the QA pipeline and a response isprovided containing one or more answers to the question. Semanticcontent is content based on the relation between signifiers, such aswords, phrases, signs, and symbols, and what they stand for, theirdenotation, or connotation. In other words, semantic content is contentthat interprets an expression, such as by using Natural LanguageProcessing.

As will be described in greater detail hereafter, the QA pipelinereceives an input question, parses the question to extract the majorfeatures of the question, uses the extracted features to formulatequeries, and then applies those queries to the corpus of data. Based onthe application of the queries to the corpus of data, the QA pipelinegenerates a set of hypotheses, or candidate answers to the inputquestion, by looking across the corpus of data for portions of thecorpus of data that have some potential for containing a valuableresponse to the input question. The QA pipeline then performs deepanalysis on the language of the input question and the language used ineach of the portions of the corpus of data found during the applicationof the queries using a variety of reasoning algorithms. There may behundreds or even thousands of reasoning algorithms applied, each ofwhich performs different analysis, e.g., comparisons, natural languageanalysis, lexical analysis, or the like, and generates a score. Forexample, some reasoning algorithms may look at the matching of terms andsynonyms within the language of the input question and the foundportions of the corpus of data. Other reasoning algorithms may look attemporal or spatial features in the language, while others may evaluatethe source of the portion of the corpus of data and evaluate itsveracity.

The scores obtained from the various reasoning algorithms indicate theextent to which the potential response is inferred by the input questionbased on the specific area of focus of that reasoning algorithm. Eachresulting score is then weighted against a statistical model. Thestatistical model captures how well the reasoning algorithm performed atestablishing the inference between two similar passages for a particulardomain during the training period of the QA pipeline. The statisticalmodel is used to summarize a level of confidence that the QA pipelinehas regarding the evidence that the potential response, i.e. candidateanswer, is inferred by the question. This process is repeated for eachof the candidate answers until the QA pipeline identifies candidateanswers that surface as being significantly stronger than others andthus, generates a final answer, or ranked set of answers, for the inputquestion.

As mentioned above, QA pipeline mechanisms operate by accessinginformation from a corpus of data or information (also referred to as acorpus of content), analyzing it, and then generating answer resultsbased on the analysis of this data. Accessing information from a corpusof data typically includes: a database query that answers questionsabout what is in a collection of structured records, and a search thatdelivers a collection of document links in response to a query against acollection of unstructured data (text, markup language, etc.).Conventional question answering systems are capable of generatinganswers based on the corpus of data and the input question, verifyinganswers to a collection of questions for the corpus of data, correctingerrors in digital text using a corpus of data, and selecting answers toquestions from a pool of potential answers, i.e. candidate answers.

Content creators, such as article authors, electronic document creators,web page authors, document database creators, and the like, determineuse cases for products, solutions, and services described in suchcontent before writing their content. Consequently, the content creatorsknow what questions the content is intended to answer in a particulartopic addressed by the content. Categorizing the questions, such as interms of roles, type of information, tasks, or the like, associated withthe question, in each document of a corpus of data allows the QApipeline to more quickly and efficiently identify documents containingcontent related to a specific query. The content may also answer otherquestions that the content creator did not contemplate that may beuseful to content users. The questions and answers may be verified bythe content creator to be contained in the content for a given document.These capabilities contribute to improved accuracy, system performance,machine learning, and confidence of the QA pipeline. Content creators,automated tools, or the like, annotate or otherwise generate metadatafor providing information useable by the QA pipeline to identify thesequestion and answer attributes of the content.

Operating on such content, the QA pipeline generates answers for inputquestions using a plurality of intensive analysis mechanisms whichevaluate the content to identify the most probable answers, i.e.candidate answers, for the input question. The most probable answers areoutput as a ranked listing of candidate answers ranked according totheir relative scores or confidence measures calculated duringevaluation of the candidate answers, as a single final answer having ahighest ranking score or confidence measure, or which is a best match tothe input question, or a combination of ranked listing and final answer.

FIG. 1 depicts a schematic diagram of one illustrative embodiment of acognitive system 100 implementing a request processing pipeline 108,which in some embodiments may be a question answering (QA) pipeline, ina computer network 102. For purposes of the present description, it willbe assumed that the request processing pipeline 108 is implemented as aQA pipeline that operates on structured and/or unstructured requests inthe form of input questions. One example of a question processingoperation Which may be used in conjunction with the principles describedherein is described in U.S. Patent Application Publication No.2011/0125734, which is herein incorporated by reference in its entirety.The cognitive system 100 is implemented on one or more computing devices104A-C (comprising one or more processors and one or more memories, andpotentially any other computing device elements generally known in theart including buses, storage devices, communication interfaces, and thelike) connected to the computer network 102. For purposes ofillustration only, FIG. 1 depicts the cognitive system 100 beingimplemented on computing device 104A only, but as noted above thecognitive system 100 may be distributed across multiple computingdevices, such as a plurality of computing devices 104A-C. The network102 includes multiple computing devices 104A-C, which may operate asserver computing devices, and 110-112 which may operate as clientcomputing devices, in communication with each other and with otherdevices or components via one or more wired and/or wireless datacommunication links, where each communication link comprises one or moreof wires, routers, switches, transmitters, receivers, or the like. Insome illustrative embodiments, the cognitive system 100 and network 102enables question processing and answer generation (QA) functionality forone or more cognitive system users via their respective computingdevices 110-112. In other embodiments, the cognitive system 100 andnetwork 102 may provide other types of cognitive operations including,but not limited to, request processing and cognitive response generationwhich may take many different forms depending upon the desiredimplementation, e.g., cognitive information retrieval,training/instruction of users, cognitive evaluation of data, or thelike. Other embodiments of the cognitive system 100 may be used withcomponents, systems, sub-systems, and/or devices other than those thatare depicted herein.

The cognitive system 100 is configured to implement a request processingpipeline 108 that receive inputs from various sources. The requests maybe posed in the form of a natural language question, natural languagerequest for information, natural language request for the performance ofa cognitive operation, or the like. For example, the cognitive system100 receives input from the network 102, a corpus or corpora ofelectronic documents 106, cognitive system users, and/or other data andother possible sources of input. In one embodiment, some or all of theinputs to the cognitive system 100 are routed through the network 102.The various computing devices 104A-C on the network 102 include accesspoints for content creators and cognitive system users. Some of thecomputing devices 104A-C include devices for a database storing thecorpus or corpora of data 106 (which is shown as a separate entity inFIG. 1 for illustrative purposes only). Portions of the corpus orcorpora of data 106 may also be provided on one or more other networkattached storage devices, in one or more databases, or other computingdevices not explicitly shown in FIG. 1. The network 102 includes localnetwork connections and remote connections in various embodiments, suchthat the cognitive system 100 may operate in environments of any size,including local and global, e.g., the Internet.

In one embodiment, the content creator creates content in a document ofthe corpus or corpora of data 106 for use as part of a corpus of datawith the cognitive system 100. The document includes any file, text,article, or source of data for use in the cognitive system 100.Cognitive system users access the cognitive system 100 via a networkconnection or an Internet connection to the network 102, and inputquestions/requests to the cognitive system 100 that areanswered/processed based on the content in the corpus or corpora of data106. In one embodiment, the questions/requests are formed using naturallanguage. The cognitive system 100 parses and interprets thequestion/request via a pipeline 108, and provides a response to thecognitive system user, e.g., cognitive system user 110, containing oneor more answers to the question posed, response to the request, resultsof processing the request, or the like. In some embodiments, thecognitive system 100 provides a response to users in a ranked list ofcandidate answers/responses while in other illustrative embodiments, thecognitive system 100 provides a single final answer/response or acombination of a final answer/response and ranked listing of othercandidate answers/responses.

The cognitive system 100 implements the pipeline 108 which comprises aplurality of stages for processing an input question/request based oninformation obtained from the corpus or corpora of data 106. Thepipeline 108 generates answers/responses for the input question orrequest based on the processing of the input question/request and thecorpus or corpora of data 106. The pipeline 108 will be described ingreater detail hereafter with regard to FIG. 3.

In some illustrative embodiments, the cognitive system 100 may be theIBM Watson™ cognitive system available from International BusinessMachines Corporation of Armonk, N.Y., which is augmented with themechanisms of the illustrative embodiments described hereafter. Asoutlined previously, a pipeline of the IBM Watson™ cognitive systemreceives an input question or request which it then parses to extractthe major features of the question/request, which in turn are then usedto formulate queries that are applied to the corpus or corpora of data106. Based on the application of the queries to the corpus or corpora ofdata 106, a set of hypotheses, or candidate answers/responses to theinput question/request, are generated by looking across the corpus orcorpora of data 106 for portions of the corpus or corpora of data 106(hereafter referred to simply as the corpus 106) that have somepotential for containing a valuable response to the inputquestion/response (hereafter assumed to be an input question). Thepipeline 108 of the IBM Watson™ cognitive system then performs deepanalysis on the language of the input question and the language used ineach of the portions of the corpus 106 found during the application ofthe queries using a variety of reasoning algorithms.

The scores obtained from the various reasoning algorithms are thenweighted against a statistical model that summarizes a level ofconfidence that the pipeline 108 of the IBM Watson™ cognitive system100, in this example, has regarding the evidence that the potentialcandidate answer is inferred by the question. This process is berepeated for each of the candidate answers to generate ranked listing ofcandidate answers which may then be presented to the user that submittedthe input question, e.g., a user of client computing device 110, or fromwhich a final answer is selected and presented to the user. Moreinformation about the pipeline 108 of the IBM Watson™ cognitive system100 may be obtained, for example, from the IBM Corporation website, IBMRedbooks, and the like. For example, information about the pipeline ofthe IBM Watson™ cognitive system can be found in Yuan et al., “Watsonand Healthcare,” IBM developerWorks, 2011 and “The Era of CognitiveSystems: An Inside Look at IBM Watson and How it Works” by Rob High, IBMRedbooks, 2012.

As noted above, while the input to the cognitive system 100 from aclient device may be posed in the form of a natural language question,the illustrative embodiments are not limited to such. Rather, the inputquestion may in fact be formatted or structured as any suitable type ofrequest which may be parsed and analyzed using structured and/orunstructured input analysis, including but not limited to the naturallanguage parsing and analysis mechanisms of a cognitive system such asIBM Watson™, to determine the basis upon which to perform cognitiveanalysis and providing a result of the cognitive analysis. In the caseof a healthcare based cognitive system, this analysis may involveprocessing patient medical records, medical guidance documentation fromone or more corpora, and the like, to provide a healthcare orientedcognitive system result.

In the context of the present invention, cognitive system 100 mayprovide a cognitive functionality for assisting with healthcare basedoperations. For example, depending upon the particular implementation,the healthcare based operations may comprise patient diagnostics,medical treatment recommendation systems, medical practice managementsystems, personal patient care plan generation and monitoring, patientelectronic medical record (EMR) evaluation for various purposes, such asfor identifying patients that are suitable for a medical trial or aparticular type of medical treatment, or the like. Thus, the cognitivesystem 100 may be a healthcare cognitive system 100 that operates in themedical or healthcare type domains and which may process requests forsuch healthcare operations via the request processing pipeline 108 inputas either structured or unstructured requests, natural language inputquestions, or the like. In one illustrative embodiment, the cognitivesystem 100 implements a drug response estimation engine 120.

As shown in FIG. 1, the cognitive system 100 is further augmented, inaccordance with the mechanisms of the illustrative embodiments, toinclude logic implemented in specialized hardware, software executed onhardware, or any combination of specialized hardware and softwareexecuted on hardware, for implementing a drug response estimation engine120.

As noted above, the mechanisms of the illustrative embodiments arerooted in the computer technology arts and are implemented using logicpresent in such computing or data processing systems. These computing ordata processing systems are specifically configured, either throughhardware, software, or a combination of hardware and software, toimplement the various operations described above. As such, FIG. 2 isprovided as an example of one type of data processing system in whichaspects of the present invention may be implemented. Many other types ofdata processing systems may be likewise configured to specificallyimplement the mechanisms of the illustrative embodiments.

FIG. 2 is a block diagram of an example data processing system in whichaspects of the illustrative embodiments are implemented. Data processingsystem 200 is an example of a computer, such as server 104 or client 110in FIG. 1, in which computer usable code or instructions implementingthe processes for illustrative embodiments of the present invention arelocated. In one illustrative embodiment, FIG. 2 represents a servercomputing device, such as a server 104, which, which implements acognitive system 100 and QA system pipeline 108 augmented to include theadditional mechanisms of the illustrative embodiments describedhereafter.

In the depicted example, data processing system 200 employs a hubarchitecture including North Bridge and Memory Controller Hub (NB/MCH)202 and South Bridge and Input/Output (I/O) Controller Hub (SB/ICH) 204.Processing unit 206, main memory 208, and graphics processor 210 areconnected to NB/MCH 202. Graphics processor 210 is connected to NB/MCH202 through an accelerated graphics port (AGP).

In the depicted example, local area network (LAN) adapter 212 connectsto SB/ICH 204. Audio adapter 216, keyboard and mouse adapter 220, modem222, read only memory (ROM) 224, hard disk drive (HDD) 226, CD-ROM drive230, universal serial bus (USB) ports and other communication ports 232,and PCI/PCIe devices 234 connect to SB/ICH 204 through bus 238 and bus240. PCI/PCIe devices may include, for example, Ethernet adapters,add-in cards, and PC cards for notebook computers. PCI uses a card buscontroller, while PCIe does not. ROM 224 may be, for example, a flashbasic input/output system (BIOS).

HDD 226 and CD-ROM drive 230 connect to SB/ICH 204 through bus 240. HDD226 and CD-ROM drive 230 may use, for example, an integrated driveelectronics (IDE) or serial advanced technology attachment (SATA)interface. Super I/O (SIO) device 236 is connected to SB/ICH 204.

An operating system runs on processing unit 206. The operating systemcoordinates and provides control of various components within the dataprocessing system 200 in FIG. 2. As a client, the operating system is acommercially available operating system such as Microsoft® Windows 10®.An object-oriented programming system, such as the Java™ programmingsystem, may run in conjunction with the operating system and providescalls to the operating system from Java™ programs or applicationsexecuting on data processing system 200.

As a server, data processing system 200 may be, for example, an IBM®eServer™ System p® computer system, running the Advanced InteractiveExecutive (AIX®) operating system or the LINUX® operating system. Dataprocessing system 200 may be a symmetric multiprocessor (SMP) systemincluding a plurality of processors in processing unit 206.Alternatively, a single processor system may be employed.

Instructions for the operating system, the object-oriented programmingsystem, and applications or programs are located on storage devices,such as HDD 226, and are loaded into main memory 208 for execution byprocessing unit 206. The processes for illustrative embodiments of thepresent invention are performed by processing unit 206 using computerusable program code, which is located in a memory such as, for example,main memory 208, ROM 224, or in one or more peripheral devices 226 and230, for example.

A bus system, such as bus 238 or bus 240 as shown in FIG. 2, iscomprised of one or more buses. Of course, the bus system may beimplemented using any type of communication fabric or architecture thatprovides for a transfer of data between different components or devicesattached to the fabric or architecture. A communication unit, such asmodem 222 or network adapter 212 of FIG. 2, includes one or more devicesused to transmit and receive data. A memory may be, for example, mainmemory 208, ROM 224, or a cache such as found in NB/MCH 202 in FIG. 2.

Those of ordinary skill in the art will appreciate that the hardwaredepicted in FIGS. 1 and 2 may vary depending on the implementation.Other internal hardware or peripheral devices, such as flash memory,equivalent non-volatile memory, or optical disk drives and the like, maybe used in addition to or in place of the hardware depicted in FIGS. 1and 2. Also, the processes of the illustrative embodiments may beapplied to a multiprocessor data processing system, other than the SMPsystem mentioned previously, without departing from the spirit and scopeof the present invention.

Moreover, the data processing system 200 may take the form of any of anumber of different data processing systems including client computingdevices, server computing devices, a tablet computer, laptop computer,telephone or other communication device, a personal digital assistant(PDA), or the like. In some illustrative examples, data processingsystem 200 may be a portable computing device that is configured withflash memory to provide non-volatile memory for storing operating systemfiles and/or user-generated data, for example. Essentially, dataprocessing system 200 may be any known or later developed dataprocessing system without architectural limitation.

FIG. 3 is an example diagram illustrating an interaction of elements ofa healthcare cognitive system in accordance with one illustrativeembodiment. The example diagram of FIG. 3 depicts an implementation of ahealthcare cognitive system 300 that is configured to provide medicaltreatment recommendations for patients. However, it should beappreciated that this is only an example implementation and otherhealthcare operations may be implemented in other embodiments of thehealthcare cognitive system 300 without departing from the spirit andscope of the present invention.

Moreover, it should be appreciated that while FIG. 3 depicts the patient302 and user 306 as human figures, the interactions with and betweenthese entities may be performed using computing devices, medicalequipment, and/or the like, such that entities 302 and 306 may in factbe computing devices, e.g., client computing devices. For example, theinteractions 304, 314, 316, and 330 between the patient 302 and the user306 may be performed orally, e.g., a doctor interviewing a patient, andmay involve the use of one or more medical instruments, monitoringdevices, or the like, to collect information that may be input to thehealthcare cognitive system 300 as patient attributes 318. Interactionsbetween the user 306 and the healthcare cognitive system 300 will beelectronic via a user computing device (not shown), such as a clientcomputing device 110 or 112 in FIG. 1, communicating with the healthcarecognitive system 300 via one or more data communication links andpotentially one or more data networks.

As shown in FIG. 3, in accordance with one illustrative embodiment, apatient 302 presents symptoms 304 of a medical malady or condition to auser 306, such as a healthcare practitioner, technician, or the like.The user 306 may interact with the patient 302 via a question 314 andresponse 316 exchange where the user gathers more information about thepatient 302, the symptoms 304, and the medical malady or condition ofthe patient 302. It should be appreciated that the questions/responsesmay in fact also represent the user 306 gathering information from thepatient 302 using various medical equipment, e.g., blood pressuremonitors, thermometers, wearable health and activity monitoring devicesassociated with the patient such as a FitBit™, a wearable heart monitor,or any other medical equipment that may monitor one or more medicalcharacteristics of the patient 302. In some cases such medical equipmentmay be medical equipment typically used in hospitals or medical centersto monitor vital signs and medical conditions of patients that arepresent in hospital beds for observation or medical treatment.

In response, the user 302 submits a request 308 to the healthcarecognitive system 300, such as via a user interface on a client computingdevice that is configured to allow users to submit requests to thehealthcare cognitive system 300 in a format that the healthcarecognitive system 300 can parse and process. The request 308 may include,or be accompanied with, information identifying patient attributes 318.These patient attributes 318 may include, for example, an identifier ofthe patient 302 from which patient EMRs 322 for the patient may beretrieved, demographic information about the patient, the symptoms 304,and other pertinent information obtained from the responses 316 to thequestions 314 or information obtained from medical equipment used tomonitor or gather data about the condition of the patient 302. Anyinformation about the patient 302 that may be relevant to a cognitiveevaluation of the patient by the healthcare cognitive system 300 may beincluded in the request 308 and/or patient attributes 318.

The healthcare cognitive system 300 provides a cognitive system that isspecifically configured to perform an implementation specific healthcareoriented cognitive operation. In the depicted example, this healthcareoriented cognitive operation is directed to providing a treatmentrecommendation 328 to the user 306 to assist the user 306 in treatingthe patient 302 based on their reported symptoms 304 and otherinformation gathered about the patient 302 via the question 314 andresponse 316 process and/or medical equipment monitoring/data gathering.The healthcare cognitive system 300 operates on the request 308 andpatient attributes 318 utilizing information gathered from the medicalcorpus and other source data 326, treatment guidance data 324, and thepatient EMRs 322 associated with the patient 302 to generate one or moretreatment recommendation 328. The treatment recommendations 328 may bepresented in a ranked ordering with associated supporting evidence,obtained from the patient attributes 318 and data sources 322-326,indicating the reasoning as to why the treatment recommendation 328 isbeing provided and why it is ranked in the manner that it is ranked.

For example, based on the request 308 and the patient attributes 318,the healthcare cognitive system 300 may operate on the request, such asby using a QA pipeline type processing as described herein, to parse therequest 308 and patient attributes 318 to determine what is beingrequested and the criteria upon which the request is to be generated asidentified by the patient attributes 318, and may perform variousoperations for generating queries that are sent to the data sources322-326 to retrieve data, generate candidate treatment recommendationsanswers to the input question), and score these candidate treatmentrecommendations based on supporting evidence found in the data sources322-326. In the depicted example, the patient EMRs 322 is a patientinformation repository that collects patient data from a variety ofsources, e.g., hospitals, laboratories, physicians' offices, healthinsurance companies, pharmacies, etc. The patient EMRs 322 store variousinformation about individual patients, such as patient 302, in a manner(structured, unstructured, or a mix of structured and unstructuredformats) that the information may be retrieved and processed by thehealthcare cognitive system 300. This patient information may comprisevarious demographic information about patients, personal contactinformation about patients, employment information, health insuranceinformation, laboratory reports, physician reports from office visits,hospital charts, historical information regarding previous diagnoses,symptoms, treatments, prescription information, etc. Based on anidentifier of the patient 302, the patient's corresponding EMRs 322 fromthis patient repository may be retrieved by the healthcare cognitivesystem 300 and searched/processed to generate treatment recommendations328.

The treatment guidance data 324 provides a knowledge base of medicalknowledge that is used to identify potential treatments for a patientbased on the patient's attributes 318 and historical informationpresented in the patient's EMRs 322. This treatment guidance data 324may be obtained from official treatment guidelines and policies issuedby medical authorities, e.g., the American Medical Association, may beobtained from widely accepted physician medical and reference texts,e.g., the Physician's Desk Reference, insurance company guidelines, orthe like. The treatment guidance data 324 may be provided in anysuitable form that may be ingested by the healthcare cognitive system300 including both structured and unstructured formats.

In some cases, such treatment guidance data 324 may be provided in theform of rules that indicate the criteria required to be present, and/orrequired not to be present, for the corresponding treatment to beapplicable to a particular patient for treating a particular symptom ormedical malady/condition. For example, the treatment guidance data 324may comprise a treatment recommendation rule that indicates that for atreatment of Decitabine, strict criteria for the use of such a treatmentis that the patient 302 is less than or equal to 60 years of age, hasacute myeloid leukemia (AML), and no evidence of cardiac disease. Thus,for a patient 302 that is 59 years of age, has AML, and does not haveany evidence in their patient attributes 318 or patient EMRs indicatingevidence of cardiac disease, the following conditions of the treatmentrule exist:

Age<=60 years=59 (MET);

Patient has AML=AML (MET); and

Cardiac Disease=false (MET)

Since all of the criteria of the treatment rule are met by the specificinformation about this patient 302, then the treatment of Decitabine isa candidate treatment for consideration for this patient 302. However,if the patient had been 69 years old, the first criterion would not havebeen met and the Decitabine treatment would not be a candidate treatmentfor consideration for this patient 302. Various potential treatmentrecommendations may be evaluated by the healthcare cognitive system 300based on ingested treatment guidance data 324 to identify subsets ofcandidate treatments for further consideration by the healthcarecognitive system 300 by scoring such candidate treatments based onevidential data obtained from the patient EMRs 322 and medical corpusand other source data 326.

For example, data mining processes may be employed to mine the data insources 322 and 326 to identify evidential data supporting and/orrefuting the applicability of the candidate treatments to the particularpatient 302 as characterized by the patient's patient attributes 318 andEMRs 322. For example, for each of the criteria of the treatment rule,the results of the data mining provides a set of evidence that supportsgiving the treatment in the cases where the criterion is “MET” and incases where the criterion is “NOT MET.” The healthcare cognitive system300 processes the evidence in accordance with various cognitive logicalgorithms to generate a confidence score for each candidate treatmentrecommendation indicating a confidence that the corresponding candidatetreatment recommendation is valid for the patient 302. The candidatetreatment recommendations may then be ranked according to theirconfidence scores and presented to the user 306 as a ranked listing oftreatment recommendations 328. In some cases, only a highest ranked, orfinal answer, is returned as the treatment recommendation 328. Thetreatment recommendation 328 may be presented to the user 306 in amanner that the underlying evidence evaluated by the healthcarecognitive system 300 may be accessible, such as via a drilldowninterface, so that the user 306 may identify the reasons why thetreatment recommendation 328 is being provided by the healthcarecognitive system 300.

In accordance with the illustrative embodiments herein, the healthcarecognitive system 300 is augmented to include a drug response estimationengine 320. In this embodiment, drug response estimation engine 320 usespatient demographics and diagnoses for building a patient similaritynetwork. Drug response estimation engine 320 uses that patientsimilarity network for regularizing the baseline regularizationframework via network LASSO regression analysis. Drug responseestimation engine 320 also optimizes the parameters of the framework bylearning from observed patients in the real-world evidence to assess theresponses of drugs on each patient. Drug response estimation engine 320interprets the obtained parameters for drug response and groups ofpatients to identify associations between a patient's demographic andcomorbidities with the drug response (both therapeutic effects andadverse drug reactions). Drug response estimation engine 320 usesbaseline regularization framework with optimized parameters forpredicting the drug response for each patient and for each time point ofthe real-world evidence.

While FIG. 3 is depicted with an interaction between the patient 302 anda user 306, which may be a healthcare practitioner such as a physician,nurse, physician's assistant, lab technician, or any other healthcareworker, for example, the illustrative embodiments do not require such.Rather, the patient 302 may interact directly with the healthcarecognitive system 300 without having to go through an interaction withthe user 306 and the user 306 may interact with the healthcare cognitivesystem 300 without having to interact with the patient 302. For example,in the first case, the patient 302 may be requesting 308 treatmentrecommendations 328 from the healthcare cognitive system 300 directlybased on the symptoms 304 provided by the patient 302 to the healthcarecognitive system 300. Moreover, the healthcare cognitive system 300 mayactually have logic for automatically posing questions 314 to thepatient 302 and receiving responses 316 from the patient 302 to assistwith data collection for generating treatment recommendations 328. Inthe latter case, the user 306 may operate based on only informationpreviously gathered and present in the patient EMR 322 by sending arequest 308 along with patient attributes 318 and obtaining treatmentrecommendations in response from the healthcare cognitive system 300.Thus, the depiction in FIG. 3 is only an example and should not beinterpreted as requiring the particular interactions depicted when manymodifications may be made without departing from the spirit and scope ofthe present invention. It should be appreciated, however, that at notime should the treatment itself be administered to the patient 302without prior approval of the healthcare professional treating thepatient, i.e. final determinations as to treatments given to a patientwill always fall on the healthcare professional with the mechanisms ofthe illustrative embodiments serving only as an advisory tool for thehealthcare professional (user 306) and/or patient 302.

As mentioned above, the healthcare cognitive system 300 may include arequest processing pipeline, such as request processing pipeline 108 inFIG. 1, which may be implemented, in some illustrative embodiments, as aQuestion Answering (QA) pipeline. The QA pipeline may receive an inputquestion, such as “what is the appropriate treatment for patient P?” ora request, such as “diagnose and provide a treatment recommendation forpatient P.”

The illustrative embodiments provide a mechanism for making predictionsabout drug responses for each patient separately to provide key insightsand generate hypotheses about personalized therapeutic effects andadverse drug reactions. Examples of predicted associations include: 1)drug responses for each patient for each specific time; 2) potentialhypotheses about new therapeutic effects and adverse drug reactions; and3) associations between patients' characteristics and demographics withthe obtained drug responses. This could be applied to pharmacovigilancerisk management, keeping patients with characteristics suggesting theyare especially at-risk for an adverse drug reaction (ADR) safe while,for those patient segments that do not have those characteristics,maintaining access to the recommended drug treatments.

The illustrative embodiments propose a personalized drug responseestimation engine to identify unique response patterns of eachindividual patient using the longitudinal patient record. In particular,the personalized drug response estimation engine uses separateparameters for each individual patient which representing the drugeffects on an outcome of interest.

The illustrative embodiments introduce a linear model that can accountfor the patients' heterogeneity in terms of how they respond to aparticular set of drugs, which generalizes the original baselineregularization model.

The illustrative embodiments incorporate several regularization schemesas additional loss functions, so that over-parameterization ofpersonalized drug response model can be avoided. Using one such networkregularization approach, the personalized drug response estimationengine can further cluster the patients automatically into multiplecoherent groups. The personalized drug response estimation engine usesan iterative gradient descend based approach for solving the convexoptimization problem.

FIGS. 4A and 4B are graphs illustrating a longitudinal patient historyof drug exposures in accordance with an illustrative embodiment. Anexample of the longitudinal patient's record is represented in FIG. 4A.Assume that there are N patients in the EHR data with at least onerecord of the lab test measurement under consideration. Here, denotey_(ij)∈IR as the lab test measurement of the i^(th) patient, wherei∈{1,2, . . . ,N}, at the j^(th) time point taken among a total numberof j_(i) lab test measurement, i.e., j∈{1,2, . . . ,j_(i)}. Also denotethe drug exposures of M drugs for i^(th) patient until the j^(th) timepoint as a vector x_(ij)∈IR^(M). Each entry of this vector, x_(ijm)represents the response of the m^(th) drug for m∈{1,2, . . . ,M}.

Also represent the diagnostic codes for patient i at time point j alsowith a binary variable D_(ijd)∈IR^(D) for the d-th ICD-9 diagnosticvariables coming from a total number of D diagnostic codes. Also denotethe demographic information available for each patient i as G_(i)∈IR^(G)for total number of G demographic information.

The outcome of interest for each patient i is shown in the y-axis asdenoted by y_(ij) at j^(th) time point. In FIG. 4A, box 401 representsthe diagnostic exposure and boxes 402, 403 represent the drug exposuresof the same patient at different time points. Also, the α_(i) representsthe inherent baseline amount of outcome of interest for the samepatient, without being exposed to drugs. This is to handle the inherentvariations among patients in terms of their baseline amount of certainclinical outcome of interest (e.g., laboratory test measurements,cholesterol level, etc.), due to their unobserved but fixed confounderfactors such as their genetic background and demographic background. Thegoal of illustrative embodiments can be illustrated using the rightpanel of the following figure.

As shown in FIG. 4B, the illustrative embodiments assess the effect ofchange in drug exposures (Dx_(ij)) due to the change in the test of labtest results (Dy_(ij)) that are beyond the baseline amount of laboratorymeasurement (α_(i)) for each patient i at each time point j. This willlead to learn the personalized drug effect for each individual patienton the outcome of interest.

Most of the self-controlled case series (SCCC) model assumes that themeasurement level of a patient obtained at a particular time isinfluenced by the joint effect of the baseline laboratory testmeasurement and the exposures of drugs that the patient took until thattime point. The intuition behind the incorporating the baseline effectfor each individual patient is to address the issue of existingvariations among different patient groups for a particular laboratoryresults due to their inherent predisposition towards certain clinicalcondition (e.g., south Asian population have higher level of lipidprofiles). Besides such time-invariant baseline effect, the baselineeffect present among the laboratory test measurements can also varyacross time periods for each patient. Indeed, many confounding factors,both unobserved (e.g., co-morbid conditions) and observed (e.g., age orweight gains) can alter the laboratory responses of otherwise healthysubjects significantly over such a long period of observationsirrespective of the drug exposure. The effects of drug response, x_(ij),towards the laboratory results, y_(ij), including both time-invariantand time-variant baselines, can be modeled using a fixed effect model asfollows:

y _(ij) |x _(ij)=α_(i) +t _(ij) +w ^(T) x _(ij)+ε_(ij) ,

N(0,σ²)

where,

w=[w ₁ w ₂ . . . w _(M)]^(T) , x _(ij)=[x _(ij1) x _(ij2) . . . x_(ijM)]^(T),

Here, α_(i)∈IR is the patient specific unobserved and time-invariantparameter representing the baseline effect of i^(th) patient on thelaboratory test measurements y_(ij), irrespective of time point j, drugexposures x_(ij), and other patients. w is an M×1 vector with values ofw_(m), m∈{1,2, . . . M}, which represents the effect of m^(th) drug onthe measurement of lab test. ε_(ij) represents the independent andidentically distributed Gaussian noises with zero means and variance σ².The model also includes a time-dependent parameter t_(ij) which capturesthe deviation of the measurement at j_(th) point of i^(th) patient fromthe baseline effect α_(i).

which leads to solving the following least square problem:

$\underset{\alpha,\; w,\; t}{\arg \; \min}\frac{1}{2}{{y - {\begin{bmatrix}S & X & I\end{bmatrix}\begin{bmatrix}\alpha \\w \\t\end{bmatrix}}}}_{2}^{2}$ ${where},{\alpha = \begin{bmatrix}\alpha_{1} & \alpha_{2} & \ldots & \alpha_{N}\end{bmatrix}^{T}},{y = \begin{bmatrix}y_{11} & \ldots & y_{1J_{1}} & \ldots & y_{N\; 1} & \ldots & y_{{NJ}_{N}}\end{bmatrix}^{T}},{X = \begin{bmatrix}x_{11} & \ldots & x_{1J_{1}} & \ldots & x_{N\; 1} & \ldots & x_{{NJ}_{N}}\end{bmatrix}^{T}},{S = {{diag}( {1_{1},1_{2},\ldots \mspace{14mu},1_{N}} )}},{t = \lbrack {t_{11\mspace{11mu}}\ldots \mspace{14mu} t_{{1J_{1}}\mspace{11mu}}\ldots \mspace{14mu} t_{N\; 1}\mspace{14mu} \ldots \mspace{14mu} t_{{NJ}_{N}}} \rbrack^{T}}$

Here, the mechanism stacks all lab test measurements of all patientsinto a column vector y with the dimension of J×1, where J is the totalnumber of lab test measurements from all patients, i.e.,

$J = {\sum\limits_{{i =}\;}^{N}{J_{i}.}}$

Similarly, all the drug exposures are summarized in the matrixX∈IR^(J×M). Also, S is a block diagonal matrix with the dimension ofJ×N, where 1_(i) is a J_(i)×1 vector with all components being 1. α canrepresent the baseline non-random baseline laboratory measurements ofall patients. Also, I_(J×J) is the identity matrix and both α and t arenuisance parameters, which have to be learned from the observed data.

The above mentioned fixed-effect model can only estimate the baselinenon-random effect of the laboratory test measurements for each person,but these methods cannot model the individual responses of each patientstowards the drug exposure. The objective of our method is to find thepersonalized drug responses that are associated with laboratory testmeasurement y_(ij) that are beyond the patient specific baselinelaboratory results, so that individual drug responses can be utilizedfor more refined decision making yielding personalized medicine. In thispaper, we extend the fixed effect models for estimating suchpersonalized drug effect, hence the name of the model is PersonalizedDrug Effectiveness Prediction (PerDREP). The unique assumption of thismodel is that there exist variations not only among the baselinemeasurements of laboratory results, but also among the effect of drugexposures on the laboratory test measurements for a particular patientdue to patient heterogeneity.

The linear fixed effect model can be reformulated as below using oneparameter to model the effect of one drug on one particular patient:

y _(ij) |x _(ij)=α_(i) +t _(ij) +w _(i) ^(T) x _(ij)+ε_(ij) ,

N(0,σ²)

where,

W=[w ₁ w ₂ . . . w _(N)],

w _(i)=[w _(i1) w _(i2) . . . w _(M)]^(T).

Here, the individual response of the i^(th) patient on m^(th) drug isdenoted by w_(im), where i∈[1,2, . . . ,N] and m∈[1,2, . . . ,M]. So, inthese models, both w_(i) and α_(i) are patient-specific, but unknowntime-invariant parameters representing the patient-specific effect ofdrug exposures and the baseline measurement of the laboratory testmeasurement. In order to solve this problem using linear least squareformulation, the illustrative embodiment vectorizes the drug responsematrix W into a column vector w=[w₁ ^(T) . . . w_(N) ^(T)]^(T) with thedimension of NM×1. The illustrative embodiment also rearranges thefeature matrix X of equation into a new matrix Z=[Z₁ Z₂ . . .Z_(M)]^(T), where Z_(m) is a block diagonal matrix containing all thedrug exposures of drug m as below:

${Z_{m}\begin{bmatrix}z_{1m} & \; & \; & \; \\\; & z_{2m} & \; & \; \\\mspace{11mu} & \; & \ddots & \; \\\; & \; & \; & z_{Nm}\end{bmatrix}}_{J \times N},{z_{im} = {\begin{bmatrix}x_{i\; 1m} & x_{i\; 2m} & \ldots & x_{{iJ}_{m}}\end{bmatrix}^{T}.}}$

So, if one substitutes all Z_(m) corresponding to all drugs in m∈[1,2, .. . ,M], the new feature matrix Z can be obtained with the dimension ofJ×NM:

$Z = \begin{bmatrix}z_{11} & \; & \; & \; & \; & z_{1M} & \; & \; & \; \\\; & z_{21} & \; & \; & \ldots & \; & z_{2M} & \; & \; \\\; & \; & \ddots & \; & \; & \; & \; & \ddots & \; \\\; & \; & \; & z_{N\; 1} & \; & \; & \; & \; & z_{NM}\end{bmatrix}$

The illustrative embodiment reformulates the personalization drugeffectiveness prediction problem as the linear least square formulationas below:

${\underset{\alpha,\; W}{\arg \; \min}\; {\mathcal{L}_{1}( {\alpha,W} )}} = {\underset{\alpha,\; W,\; t}{\arg \; \min}\frac{1}{2}{{y - {\begin{bmatrix}S & Z & I\end{bmatrix}\begin{bmatrix}\alpha \\W \\t\end{bmatrix}}}}_{2}^{2}}$

This least square regression problem has total number of J samples,however, the model complexity increases as it has to learn MN+N+Jparameters. In order to avoid over-fitting, the illustrative embodimentimposes several regularization techniques on this model as described innext few subsections.

The illustrative embodiment introduces a few assumptions to the PerDREPmodes using temporal smoothness of the consecutive responses oflaboratory tests of patients similar to the baseline regularizationmethod. Without loss of generalizablity, let us consider two consecutivelaboratory measurements of the patient i as y_(ij) and y_(i(j−1)) thatwere taken on day π_(ij) and π_(i(j+1)), respectively. Now, if the twoadjacent pairs are closer in time, i.e., π_(i(j+1))−π_(ij)≤δ for apredefined threshold δ and the drug exposures remain constant in thatperiod, then the changes on test measurements y_(i(j+1))−y_(ij) is dueto the confounders within the time period δ. Since the effect oftime-varying confounders such as age do not fluctuate over a short timeperiod, a reasonable assumption will be that the changes in the baselineeffect should be small, i.e.,|(α_(i)−t_(i(j+1)))−(α_(i)−t_(i(j+1)))|=|(t_(i(j+1))−t_(ij)| is small.Using this assumption, a regularization term can be incorporated intothe model based on fused lasso penalty on the consecutive baselineparameters.

A slightly stricter assumption can be introduced in the model above byconsidering that the consecutive test measurements that are within δtime period have same baseline effect, i.e.,|π_(i(j+1))−π_(ij)|≤δ⇒t_(ij)=t_(i(j+1)), for a small parameter. Then,from the above,

E[y _(i(j+1)) −y _(ij) |x _(i(j+1)) −x _(ij)]=w _(i) ^(T)(x _(i(j+1)) −x_(ij))

where, all the nuisance parameters are eliminated and the change in thelaboratory test measurements only depend on the W, therefore, reducesthe number of parameters to be estimated drastically into MN. Note thatalthough this types of model adopt stricter assumptions on baselineparameters with fewer parameters than the fused lasso based baselineregularization approach, they still achieve almost similar performancesto the baseline regularization approach. This was demonstrated in anon-personalized fixed-effect model. Since the main focus of theillustrative embodiment is on learning a personalized drug responseprediction from large-scale EHR data, we adopt the stricter assumptionsin our model without loss of efficiency.

Given this assumption, we can reformulate our learning problem aslearning the effect of changes of consecutive output given any changesof drug exposure. This is illustrated in FIG. 4B, where the change oftwo consecutive test measures is modeled as the direct response to thechanges in drug intakes, since the baselines did not change within the δtime period.

Now, one can construct a cohort by considering only those patients thathave at least one pair of two consecutive laboratory test measurementswithin the time period. Note that this cohort will also solve the issueof irregularities in temporal dimension as described earlier. In thiscohort, one can reformulate the linear learning problem as follows:

${\underset{W}{\arg \; \min}\; {\mathcal{L}_{1}(W)}} = {\underset{W}{\arg \; \min}\frac{1}{2}{{{D^{\delta}y} - {D^{\delta}{ZW}}}}_{2}^{2}}$

Here, D^(δ) is a sparse matrix with dimension s×J containing only 0 or±1 entries, where s is the total number of consecutive pairs of testmeasurements that are within δ time period in the whole cohort. Thepurpose of D^(δ) is to create a first difference matrix from theobservational data, i.e., when each row of D^(δ) is multiplied with y,the new vector will contain the difference of the later measurement fromthe earlier measurement. For example, difference matrix for patient i isD_(i) ^(δ)∈IR^(s) ^(i) ^(×J), where s_(i) is the total number ofconsecutive pairs within δ period. For each k^(th) consecutive pair<y_(ij), y_(i(j+1))> for k∈[1,2, . . . ,s_(i)], the corresponding row ofD_(i) ^(δ) will be [0, . . . ,0,−1,1,0, . . . ,0] with −1 and 1 inj^(th) and J^(th) positions respectively. Now, D^(δ)=diag(D₁ ^(δ), . . .,D_(N) ^(δ)), where

$s = {\sum\limits_{i}{s_{i}.}}$

The least square regression problem has a total number of J samples,where the total number of MN parameters must be learned. EMR data areoften high-dimensional, where large number of samples (N) are consideredfor a particular cohort and large number of drugs (M) prescribed forthose patients with diverse diagnostic backgrounds. On the other hand,each sample contains a few number of consecutive laboratory testmeasurements that are within δ, which still leads toover-parameterization.

To overcome above mentioned issue, the illustrative embodiment furtherregularizes the W, which has N rows and M columns corresponding topatients and drugs, respectively. In particular, the illustrativeembodiment imposes the regularization on the drug effectiveness withineach sample so that feature selection can be performed simultaneouslyfor easier model interpretation. The easiest way to impose sparsity willbe to impose

₁ penalty on all drug features of all samples as follows:

$\mathcal{L}_{2} = {\lambda_{1}\frac{1}{2}{\sum\limits_{i = 1}^{N}{w_{i}}_{1}}}$

However, such heavy regularization on all parameters will lead to manysample weights being completely zero due to the small number of samplesavailable in the dataset. Rather we want to select a few drugs for mostof the patients, so that we can interpret the such drug effectsclinically based on other properties of the patients such as diagnosticand demographic background. Therefore, the illustrative embodimentconsiders mixed-type regularization using both

₁ and

₂ that have been used successfully in many domains, where somepredefined group structures among the variables are available. Althoughthe definition of group is not directly applicable in our case, still wecan consider each sample weight vector w_(i) as a group (in total Ngroups).

In the case of high dimensional learning, the illustrative embodimentassumes that there exists intra-group sparsity, i.e.,

₁ regularization is applied on the individual drug exposure featureswithin each sample (i.e., w_(i)), while inter-group (samples)non-sparsity is achieved by imposing a

₂ structure on the parameters obtained from all samples. This type ofmixed

_(1,2) or exclusive regularization can be defined as follows:

$\mathcal{L}_{2} = {\lambda_{1}\frac{1}{2}{\sum\limits_{i = 1}^{N}{w_{i}}_{1}^{2}}}$

where λ₁≥0 is a hyper-parameter in the model. The square of

₁ above will guarantee that all of the sample weights will remainnon-zero (i.e., w_(i)≠0).

The linear least-square formulation further assumes the personalizeddrug responses of a particular patient are independent of other patient.However, this assumption is not true in EMR, because patients havingsimilar background should have similar types of drug responses. Forexample, a particular group of patients with kidney failure may respondto a drug used to lower HbA1c in a different degree than the patientgroup with chronic heart diseases. Based on this observation, theillustrative embodiment aims to further regularize the drug responses oftwo patients based on their similarity in terms of their backgroundinformation such as diagnostic profile, demographic backgrounds and soon.

Consider a graph R∈IR^(N×N), whose elements [R]_(i,i′)=r_(ii′)≥0 is acoefficient representing the relationship between each pair of patientsi and i′ for i∈{1,2, . . . ,N} and i′∈{1,2, . . . ,N}. This graph can becomputed using any similarity measure on the background information ofpatients i and i′ such as their demographic information G_([i.]) andG_([i′.]), or their diagnostic profiles D_([i.]) and D_([i′.]), or bothby combined the individual similarity scores. Assume here that R is anundirected graph (i.e., R=R^(T)) and the diagonal elements of R arezero, i.e., r_(ii′)=0 for all i∈{1,2, . . . N}. Based on suchrelatedness of a pair of patients and (i and i′) r_(ii′), theillustrative embodiment imposes a network regularizer on thecorresponding two vectors of w_(i) and w′_(i) as follows:

${{{{{\mathcal{L}_{3} = {\lambda_{2}\frac{1}{2}{\sum\limits_{i,{i^{\prime} = 1}}^{N}r_{i,i^{\prime}}}}}}w_{i}} - w_{i}^{\prime}}}_{2}$

where λ₂≥0 is another regularization hyper-parameter.

If one combines all our assumptions described above, one gets the finalformulation of the Personalized Drug Effectiveness Prediction model asbelow:

${\underset{W}{\arg \; \min}\; \mathcal{L}} = {{\mathcal{L}_{1} + \mathcal{L}_{2} + \mathcal{L}_{3}} = {{\underset{W}{\arg \; \min}{{{Dy} - {DZ}_{w}}}_{2}^{2}} + {\lambda_{1}{\sum\limits_{i = 1}^{N}{w_{i}}_{1}^{2}}} + {\lambda_{2}{\sum\limits_{i > i^{\prime}}^{N}{\sum\limits_{i^{\prime} = 1}^{N - 1}{r_{{ii}^{\prime}}{{{w_{i} - w_{i^{\prime}}}}_{2}.}}}}}}}$

Here, λ₁ and λ₂ are the hyper-parameters. λ₁ controls the exclusivelasso penalty and λ₂ controls the network lasso penalty. Moreimportantly, these two types of regularization when combined togethercan provide nice model interpretations by learning multiple localpredictive models. If λ₂ is sufficiently large, then we can efficientlycluster the samples into multiple groups based on the similarities ofw′_(i)s. More specifically, when ∥w_(i)−w_(i′)∥₂ is too small(preferably zero), then we can consider that i^(th) and i′^(th) patientsbelong to the same clusters. At the same time outliers tend to formtheir own clusters that are very distant from the other normal clustersin terms of their average drug response co-efficients. Furthermore, ifλ₁ sparsity parameter is sufficiently large, then it helps to selectmultiple groups of drugs where each group of drugs can correspondlocally either to an individual sample or to the corresponding clusterto which the individual sample belongs.

The PerDREP problem as formulated above is a convex optimization problemwhere a global solution of W is available. We use a recently proposedlocalized lasso approach to solve such large number of parameters usingan iterative least square method. One of the advantage of such localizedlasso optimization problem is that it does not require any tuningparameter and guaranteed to converge to the optimal solution.

The localized lasso approach first derives some intermediate quantitiesbased on the given design matrix Z and the relatedness graph G asfollows:

${C = \begin{bmatrix}c_{11} & \ldots & c_{1N} \\\vdots & \ddots & \vdots \\c_{N\; 1} & \ldots & c_{NN}\end{bmatrix}},{c_{{ii}^{\prime}} = \{ {{{\begin{matrix}{{{{\sum\limits_{k = 1}^{n}\frac{r_{ik}}{{{w_{i} - w_{k}}}_{2}}} - \frac{r_{{ii}^{\prime}}}{{{w_{i} - w_{i^{\prime}}}}_{2}}},}\mspace{14mu}} & {{i = i^{\prime}},} \\{{- \frac{r_{{ii}^{\prime}}}{{{w_{i} - w_{i^{\prime}}}}_{2}}},} & {i \neq i^{\prime}}\end{matrix}.F_{g}} = {I_{d} \otimes C}},\mspace{14mu} {\lbrack F_{e} \rbrack_{ll} = {\sum\limits_{i = 1}^{n}\frac{\prod_{il}{w_{i}}_{1}}{\lfloor {W} \rfloor_{l}}}},\mspace{14mu} {H = {{\lambda_{1}F_{g}} + {\lambda_{2}{F_{e}.}}}}} }$

Here, F_(g)∈IR^(MN×MN) is a block diagonal, F_(e)∈IR^(MN×MN) is adiagonal matrix. I_(d) is a d×d identify matrix. ⊗ is the Kroneckerproduct. II_(il) is an indicator representing whether the lth element in|W| belongs to |w_(i)|. However, F_(g), F_(e) and H themselves depend onW.

Based on these new intermediate quantities, the PerDREP optimizationproblem can be reformulated as optimizing the following objectivefunction, so that W and the intermediate quantities (F_(g), F_(e) and H)can be optimized iteratively.

=∥Dy−DZW∥ ₂ ² +W ^(T)(λ₁ F _(g) ^((t))+λ₂ F _(e) ^((t)))W

where F_(g) ^((t)), F_(e) ^((t)) and H are the values of F_(g) and F_(e)are step t. Then, W can be estimated as follows and the process will beiterated until convergence (Algorithm 1 in FIG. 5).

W ^((t+1))←(H ^((t)))⁻¹ Z ^(T) D ^(T)(I _(n) +DZ(H ^((t)))⁻¹ Z ^(T) D^(T))¹ Dy

FIG. 6 is a block diagram illustrating a drug response estimation enginein accordance with an illustrative embodiment. Drug response estimationengine 610 is an overall framework that receives real-world evidence(RWE) data 601 as input and provides drug response estimations orpredictions 621-623 as output. RWE data 601 may include demographics,lab tests, diagnoses, and medication history, such as data from medicalcorpus and other source data 326 and patient electronic medical records(EMRs) 322 in FIG. 3.

Drug response estimation engine 610 includes patient similarity networkbuilder component 611, which uses patient demographics and diagnoses forbuilding a patient similarity network based on the information availableprior to their drug-exposure such as the patients' backgroundinformation, diagnosis history, comorbidities, etc., so that thispatient network can be leveraged during learning personalized drugresponses. The purpose of this patient similarity network is to makesure that similar patients will have similar drug responses to a certainoutcome of interest. Therefore, this patient similarity network isleveraged in the later steps.

LASSO analysis (component 612) based regularization have been applied onthe PerDREP model in two ways for ensuring model sparsity. First, theexclusive LASSO penalty ensures that the parameters learnt from themodels are sparse enough, i.e., only a few parameters of drug responsesof each patient are non-zero. Second, it uses network LASSO approach forthe patient similarity network to regularize the two drug responseparameters of two similar patient.

A statistical model is built, such as by using a regularizedfixed-effect model, to leverage the patient similarity network such thatdrug responses for new patients may be predicted based on thestatistical model. In particular LASSO regression analysis component 612optimizes the following objective function:

${\underset{w}{\arg \; \min}{{{Dy} - {DZw}}}_{2}^{2}} + {\lambda_{1}{\sum\limits_{i\; > \; i^{\prime}}^{N}{\sum\limits_{i^{\prime} = 1}^{N - 1}{r_{{ii}^{\prime}}{{w_{i} - w_{i^{\prime}}}}_{2}}}}} + {\lambda_{2}{\sum\limits_{i = 1}^{N}{w_{i}}_{1}^{2}}}$

Here, the w_(i) vector denotes the drug response for each individualpatient i, and the r_(ii′) represents the patient similarity betweeni^(th) and i′^(th) patients that is obtained from the previous step.Specifically, the similarity network of the patients is used forregularizing the corresponding two drug response vectors, w_(i) andw_(i′). The last term in the above-mentioned objective function is tomake the obtained drug response features more interpretable by selectingonly a few of the most important drug responses for each patient. Infact, this type of regularization will perform feature selection foreach patient simultaneously. Finally, LASSO regression analysiscomponent 612 uses the baseline regularization framework using a convexoptimization framework to learn the parameters in the above mentionedobjective function that predicts the drug response for each patient andfor each time point of the real world evidence. Note that the learnedparameters denoting drug response can be both positive and negativerepresenting the indication and adverse drug reaction for a particulardrug on a particular patient.

Patient clustering component 613 interprets the obtained parameters fordrug responses and groups of patients to find associations betweenpatient demographics and comorbidities with the drug responses. We usedhierarchical clustering with K=10 with cosine similarities. The drugresponse estimation engine 610 provides more interpretation forpersonalized medicine by further analyzing the obtained drug responsesof each patient (w_(i)) and the background information available fromreal-world evidence data for the same particular patient. Patientclustering component 613 interprets the obtained parameters for drugresponses and groups of patients to identify associations betweenpatient's demographic and comorbidities. In particular, a clusteringmechanism is applied to find groups of patients that have similar drugresponses and similar background information. This will provide furtherinterpretation of the observed phenomena of the specific drug responsesrelating to a certain kind of diagnosis and demographic background,which can be leveraged for clinical decision making in personalizedmedicine.

Drug response estimation engine 610 generates significant drug responsesfor indications and adverse drug reactions (ADRs) 621. The significantdrug responses can be obtained by considering the non-zero co-efficient(w_(i)) of drug response parameter.

Drug response estimation engine 610 also generates significant drugresponses in patient groups 622. These groups are the patients withsimilar drug responses for a particular laboratory test measurement,e.g., HbA1C for measuring the treatment of hyperglycemia.

Drug response estimation engine 610 generates drug response predictions623. In this prediction, the average drug responses are recorded foreach group as well.

FIG. 7 is a flowchart illustrating operation of a drug responseestimation engine in accordance with an illustrative embodiment.Operation begins (block 700), and the drug response estimation enginereceives real-world evidence with patient demographics, lab tests,diagnoses, and medication history (block 701). The drug responseestimation engine builds a patient similarity network (block 702). Thedrug response estimation engine uses the patient similarity network forregularizing the baseline regularization framework using a networklocalized LASSO approach (block 703). The drug response estimationengine then groups patients via clustering to find more specific drugresponses (block 704).

Next, the drug response estimation engine finds significant drugresponses for indication and adverse drug responses (block 705). Thedrug response estimation engine finds specific drug responses in eachpatient group (block 706). The drug response estimation engine alsopredicts drug responses for a new individual patient (block 707).Thereafter, operation ends (block 708).

Thus, the illustrative embodiments provide a mechanism to identifyresponses of drugs on a specific patient based on patient'sdemographics, diagnostics, lab tests, and medication history from realworld evidence. Compared to known solutions, the system has thefollowing main advantages: (1) learns personalized drug responses foreach individual patient from the real world evidence data; (2)effectively exploits the patients' demographic background informationand prior history for comorbidities; (3) predicts both types ofpersonalized drug responses—positive (i.e., therapeutic effects) andnegative (i.e., adverse drug reactions) for each patient; and (4)provides interpretations of the drug responses by mapping them withpatients' background demographics and diagnostics.

As noted above, it should be appreciated that the illustrativeembodiments may take the form of an entirely hardware embodiment, anentirely software embodiment or an embodiment containing both hardwareand software elements. In one example embodiment, the mechanisms of theillustrative embodiments are implemented in software or program code,which includes but is not limited to firmware, resident software,microcode, etc.

A data processing system suitable for storing and/or executing programcode will include at least one processor coupled directly or indirectlyto memory elements through a communication bus, such as a system bus,for example. The memory elements can include local memory employedduring actual execution of the program code, bulk storage, and cachememories which provide temporary storage of at least some program codein order to reduce the number of times code must be retrieved from bulkstorage during execution. The memory may be of various types including,but not limited to, ROM, PROM, EPROM, EEPROM, DRAM, SRAM, Flash memory,solid state memory, and the like.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, etc.) can be coupled to the system eitherdirectly or through intervening wired or wireless I/O interfaces and/orcontrollers, or the like. I/O devices may take many different formsother than conventional keyboards, displays, pointing devices, and thelike, such as for example communication devices coupled through wired orwireless connections including, but not limited to, smart phones, tabletcomputers, touch screen devices, voice recognition devices, and thelike. Any known or later developed I/O device is intended to be withinthe scope of the illustrative embodiments.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks, Modems, cable modems and Ethernet cards are just a few of thecurrently available types of network adapters for wired communications.Wireless communication based network adapters may also be utilizedincluding, but not limited to, 802.11 a/b/g/n wireless communicationadapters, Bluetooth wireless adapters, and the like. Any known or laterdeveloped network adapters are intended to be within the spirit andscope of the present invention.

The description of the present invention has been presented for purposesof illustration and description, and is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the describedembodiments. The embodiment was chosen and described in order to bestexplain the principles of the invention, the practical application, andto enable others of ordinary skill in the art to understand theinvention for various embodiments with various modifications as aresuited to the particular use contemplated. The terminology used hereinwas chosen to best explain the principles of the embodiments, thepractical application or technical improvement over technologies foundin the marketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method, in a data processing system comprisingat least one processor and at least one memory, the at least one memorycomprising instructions executed by the at least one processor to causethe at least one processor to implement a drug response estimationengine, wherein the drug response estimation engine operates to:receive, by the drug response estimation engine, real-world evidence fora plurality of patients; build, by a patient similarity network buildercomponent executing within the drug response estimation engine, apatient similarity network; build, by a regression analysis componentexecuting within the drug response estimation engine, a networklocalized regression analysis approach; group, by a patient clusteringcomponent executing within the drug response estimation engine, patientsbased on demographics and comorbidities to form a plurality of patientgroups; estimate, by the drug response estimation engine, drug responsesfor a given patient based on the patient similarity network, the networklocalized regression analysis approach, and the plurality of patientgroups; and output, by the drug response estimation engine, the drugresponses for the given patient.
 2. The method of claim 1, wherein thereal-world evidence comprises at least one of patient demographics, labtests, diagnoses, or medication history.
 3. The method of claim 1,wherein building the patient similarity network comprises building thepatient similarity network based on information available prior todrug-exposure.
 4. The method of claim 1, wherein building the networklocalized regression analysis approach comprises using the patientsimilarity network for regularizing the baseline regularizationframework using a network localized LASSO approach.
 5. The method ofclaim 1, wherein grouping the patients based on demographics andcomorbidities comprises applying the patient clustering component tofind groups of patients that have similar drug responses and similarbackground information.
 6. The method of claim 1, wherein estimatingdrug responses comprises estimating significant drug responses forindication and adverse drug reactions.
 7. The method of claim 1, whereinestimating drug responses comprises estimating specific drug responsesin each patient group within the plurality of patient groups.
 8. Themethod of claim 7, wherein each patient group comprises patients withsimilar drug responses for a particular laboratory test measurement. 9.The method of claim 1, wherein estimating drug responses comprisesrecording average drug responses for each group within the plurality ofpatient groups.
 10. The method of claim 1, wherein estimating drugresponses comprises generating predicted associations including at leastone of drug responses for each patient for each specific time, potentialhypotheses about new therapeutic effects and adverse drug reactions, orassociations between the given patients' characteristics anddemographics with the obtained drug responses.
 11. A computer programproduct comprising a computer readable storage medium having a computerreadable program stored therein, wherein the computer readable program,when executed on at least one processor of a data processing system,causes the data processing system to implement a drug responseestimation engine, wherein the computer readable program causes the dataprocessing system to: receive, by the drug response estimation engine,real-world evidence for a plurality of patients; build, by a patientsimilarity network builder component executing within the drug responseestimation engine, a patient similarity network; build, by a regressionanalysis component executing within the drug response estimation engine,a network localized regression analysis approach; group, by a patientclustering component executing within the drug response estimationengine, patients based on demographics and comorbidities to form aplurality of patient groups; estimate, by the drug response estimationengine, drug responses for a given patient based on the patientsimilarity network, the network localized regression analysis approach,and the plurality of patient groups; and output, by the drug responseestimation engine, the drug responses for the given patient.
 12. Thecomputer program product of claim 11, wherein the real-world evidencecomprises at least one of patient demographics, lab tests, diagnoses, ormedication history.
 13. The computer program product of claim 11,wherein building the patient similarity network comprises building thepatient similarity network based on information available prior todrug-exposure.
 14. The computer program product of claim 11, whereinbuilding the network localized regression analysis approach comprisesusing the patient similarity network for regularizing the baselineregularization framework using a network localized LASSO approach. 15.The computer program product of claim 11, wherein grouping the patientsbased on demographics and comorbidities comprises applying the patientclustering component to find groups of patients that have similar drugresponses and similar background information.
 16. The computer programproduct of claim 11, wherein estimating drug responses comprisesestimating significant drug responses for indication and adverse drugreactions.
 17. The computer program product of claim 11, whereinestimating drug responses comprises estimating specific drug responsesin each patient group within the plurality of patient groups.
 18. Thecomputer program product of claim 17, wherein each patient groupcomprises patients with similar drug responses for a particularlaboratory test measurement.
 19. The computer program product of claim11, wherein estimating drug responses comprises recording average drugresponses for each group within the plurality of patient groups.
 20. Anapparatus comprising: a processor; and a memory coupled to theprocessor, wherein the memory comprises instructions which, whenexecuted by the processor, cause the processor to implement a drugresponse estimation engine, wherein the instructions cause the processorto: receive, by the drug response estimation engine, real-world evidencefor a plurality of patients; build, by a patient similarity networkbuilder component executing within the drug response estimation engine,a patient similarity network; build, by a regression analysis componentexecuting within the drug response estimation engine, a networklocalized regression analysis approach; group, by a patient clusteringcomponent executing within the drug response estimation engine, patientsbased on demographics and comorbidities to form a plurality of patientgroups; estimate, by the drug response estimation engine, drug responsesfor a given patient based on the patient similarity network, the networklocalized regression analysis approach, and the plurality of patientgroups; and output, by the drug response estimation engine, the drugresponses for the given patient.